Learning to Spend: Model Predictive Control for Budgeting under Non-Stationary Returns
We study finite-horizon budget allocation as a closed-loop economic control problem and evaluate receding-horizon Model Predictive Control (MPC) relative to reactive budgeting policies. Budgets are allocated periodically under execution noise and operational constraints, while return efficiency may evolve over time. Using a controlled simulation framework motivated by digital marketing, we compare reactive pacing to MPC across environments with increasing degrees of non-stationarity. Our results show that non-stationarity alone does not justify predictive control. When return dynamics are stationary or evolve through unpredictable stochastic drift, MPC offers no systematic advantage over ...
| Authors | Nilavra Pathak, Smriti Shyamal, Prasant Mhasker et al. |
|---|---|
| Primary category | eess.SY |
| Published | 29 April 2026 |
| Last updated at source | 29 April 2026 |
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